1,939 research outputs found
Performance evaluation of decisionmaking organizations
Caption title.Bibliography: p. 7-8.Supported, in part, by a contract from the Office of Naval Research. N00014-85-K-0519 Supported, in part, by a contract from the Joint Directors of Laboraties through the Office of Naval Research. N00014-85-K-0782by Herve P. Hillion, Alexander H. Levis
Reachability Analysis of Time Basic Petri Nets: a Time Coverage Approach
We introduce a technique for reachability analysis of Time-Basic (TB) Petri
nets, a powerful formalism for real- time systems where time constraints are
expressed as intervals, representing possible transition firing times, whose
bounds are functions of marking's time description. The technique consists of
building a symbolic reachability graph relying on a sort of time coverage, and
overcomes the limitations of the only available analyzer for TB nets, based in
turn on a time-bounded inspection of a (possibly infinite) reachability-tree.
The graph construction algorithm has been automated by a tool-set, briefly
described in the paper together with its main functionality and analysis
capability. A running example is used throughout the paper to sketch the
symbolic graph construction. A use case describing a small real system - that
the running example is an excerpt from - has been employed to benchmark the
technique and the tool-set. The main outcome of this test are also presented in
the paper. Ongoing work, in the perspective of integrating with a
model-checking engine, is shortly discussed.Comment: 8 pages, submitted to conference for publicatio
Mean Field description of and propagation of chaos in recurrent multipopulation networks of Hodgkin-Huxley and Fitzhugh-Nagumo neurons
We derive the mean-field equations arising as the limit of a network of
interacting spiking neurons, as the number of neurons goes to infinity. The
neurons belong to a fixed number of populations and are represented either by
the Hodgkin-Huxley model or by one of its simplified version, the
Fitzhugh-Nagumo model. The synapses between neurons are either electrical or
chemical. The network is assumed to be fully connected. The maximum
conductances vary randomly. Under the condition that all neurons initial
conditions are drawn independently from the same law that depends only on the
population they belong to, we prove that a propagation of chaos phenomenon
takes places, namely that in the mean-field limit, any finite number of neurons
become independent and, within each population, have the same probability
distribution. This probability distribution is solution of a set of implicit
equations, either nonlinear stochastic differential equations resembling the
McKean-Vlasov equations, or non-local partial differential equations resembling
the McKean-Vlasov-Fokker- Planck equations. We prove the well-posedness of
these equations, i.e. the existence and uniqueness of a solution. We also show
the results of some preliminary numerical experiments that indicate that the
mean-field equations are a good representation of the mean activity of a finite
size network, even for modest sizes. These experiment also indicate that the
McKean-Vlasov-Fokker- Planck equations may be a good way to understand the
mean-field dynamics through, e.g., a bifurcation analysis.Comment: 55 pages, 9 figure
Modeling Time in Computing: A Taxonomy and a Comparative Survey
The increasing relevance of areas such as real-time and embedded systems,
pervasive computing, hybrid systems control, and biological and social systems
modeling is bringing a growing attention to the temporal aspects of computing,
not only in the computer science domain, but also in more traditional fields of
engineering.
This article surveys various approaches to the formal modeling and analysis
of the temporal features of computer-based systems, with a level of detail that
is suitable also for non-specialists. In doing so, it provides a unifying
framework, rather than just a comprehensive list of formalisms.
The paper first lays out some key dimensions along which the various
formalisms can be evaluated and compared. Then, a significant sample of
formalisms for time modeling in computing are presented and discussed according
to these dimensions. The adopted perspective is, to some extent, historical,
going from "traditional" models and formalisms to more modern ones.Comment: More typos fixe
Self-sustained irregular activity in an ensemble of neural oscillators
An ensemble of pulse-coupled phase-oscillators is thoroughly analysed in the
presence of a mean-field coupling and a dispersion of their natural
frequencies. In spite of the analogies with the Kuramoto setup, a much richer
scenario is observed. The "synchronised phase", which emerges upon increasing
the coupling strength, is characterized by highly-irregular fluctuations: a
time-series analysis reveals that the dynamics of the order parameter is indeed
high-dimensional. The complex dynamics appears to be the result of the
non-perturbative action of a suitably shaped phase-response curve. Such
mechanism differs from the often invoked balance between excitation and
inhibition and might provide an alternative basis to account for the
self-sustained brain activity in the resting state. The potential interest of
this dynamical regime is further strengthened by its (microscopic) linear
stability, which makes it quite suited for computational tasks. The overall
study has been performed by combining analytical and numerical studies,
starting from the linear stability analysis of the asynchronous regime, to
include the Fourier analysis of the Kuramoto order parameter, the computation
of various types of Lyapunov exponents, and a microscopic study of the
inter-spike intervals.Comment: 11 pages, 10 figure
Global analysis of a continuum model for monotone pulse-coupled oscillators
We consider a continuum of phase oscillators on the circle interacting
through an impulsive instantaneous coupling. In contrast with previous studies
on related pulse-coupled models, the stability results obtained in the
continuum limit are global. For the nonlinear transport equation governing the
evolution of the oscillators, we propose (under technical assumptions) a global
Lyapunov function which is induced by a total variation distance between
quantile densities. The monotone time evolution of the Lyapunov function
completely characterizes the dichotomic behavior of the oscillators: either the
oscillators converge in finite time to a synchronous state or they
asymptotically converge to an asynchronous state uniformly spread on the
circle. The results of the present paper apply to popular phase oscillators
models (e.g. the well-known leaky integrate-and-fire model) and draw a strong
parallel between the analysis of finite and infinite populations. In addition,
they provide a novel approach for the (global) analysis of pulse-coupled
oscillators.Comment: 33 page
A neuro-inspired system for online learning and recognition of parallel spike trains, based on spike latency and heterosynaptic STDP
Humans perform remarkably well in many cognitive tasks including pattern
recognition. However, the neuronal mechanisms underlying this process are not
well understood. Nevertheless, artificial neural networks, inspired in brain
circuits, have been designed and used to tackle spatio-temporal pattern
recognition tasks. In this paper we present a multineuronal spike pattern
detection structure able to autonomously implement online learning and
recognition of parallel spike sequences (i.e., sequences of pulses belonging to
different neurons/neural ensembles). The operating principle of this structure
is based on two spiking/synaptic neurocomputational characteristics: spike
latency, that enables neurons to fire spikes with a certain delay and
heterosynaptic plasticity, that allows the own regulation of synaptic weights.
From the perspective of the information representation, the structure allows
mapping a spatio-temporal stimulus into a multidimensional, temporal, feature
space. In this space, the parameter coordinate and the time at which a neuron
fires represent one specific feature. In this sense, each feature can be
considered to span a single temporal axis. We applied our proposed scheme to
experimental data obtained from a motor inhibitory cognitive task. The test
exhibits good classification performance, indicating the adequateness of our
approach. In addition to its effectiveness, its simplicity and low
computational cost suggest a large scale implementation for real time
recognition applications in several areas, such as brain computer interface,
personal biometrics authentication or early detection of diseases.Comment: Submitted to Frontiers in Neuroscienc
Coordination and Privacy Preservation in Multi-Agent Systems
This dissertation considers two key problems in multi-agent systems: coordination (including both synchronization and desynchronization) and privacy preservation.
For coordination in multi-agent systems, we focus on synchronization/desynchronization of distributed pulse-coupled oscillator (PCO) networks and their applications in collective motion coordination. Pulse-coupled oscillators were originally proposed to model synchronization in biological systems such as flashing fireflies and firing neurons. In recent years, with proven scalability, simplicity, accuracy, and robustness, the PCO based synchronization strategy has become a powerful clock synchronization primitive for wireless sensor networks. Driven by these increased applications in biological networks and wireless sensor networks, synchronization of pulse-coupled oscillators has gained increased popularity. However, most existing results address the local synchronization of PCOs with initial phases constrained in a half cycle, and results on global synchronization from any initial condition are very sparse. In our work, we address global PCO synchronization from an arbitrary phase distribution under chain or directed tree graphs. More importantly, different from existing global synchronization studies on decentralized PCO networks, our work allows heterogeneous coupling functions and perturbations on PCOs\u27 natural frequencies, and our results hold under any coupling strength between zero and one, which is crucial because a large coupling strength has been shown to be detrimental to the robustness of PCO synchronization to disturbances.
Compared with synchronization, desynchronization of PCOs is less explored. Desynchronization spreads the phase variables of all PCOs uniformly apart (with equal difference between neighboring phases). It has also been found in many biological phenomena, such as neuron spiking and fish signaling. Recently, phase desynchronization has been employed to achieve round-robin scheduling, which is crucial in applications as diverse as media access control of communication networks, realization of analog-to-digital converters, and scheduling of traffic flows in intersections. In our work, we systematically characterize pulse-coupled oscillators based decentralized phase desynchronization and propose an interaction function that is more general than existing results. Numerical simulations show that the proposed pulse based interaction function also has better robustness to pulse losses, time delays, and frequency errors than existing results.
Collective motion coordination is fundamental in systems as diverse as mobile sensor networks, swarm robotics, autonomous vehicles, and animal groups. Inspired by the close relationship between phase synchronization/desynchronization of PCOs and the heading dynamics of connected vehicles/robots, we propose a pulse-based integrated communication and control approach for collective motion coordination. Our approach only employs simple and identical pulses, which significantly reduces processing latency and communication delay compared with conventional packet based communications. Not only can heading control be achieved in the proposed approach to coordinate the headings (orientations) of motions in a network, but also spacing control for circular motion is achievable to design the spacing between neighboring nodes (e.g., vehicles or robots).
The second part of this dissertation is privacy preservation in multi-agent systems. More specifically, we focus on privacy-preserving average consensus as it is key for multi-agent systems, with applications ranging from time synchronization, information fusion, load balancing, to decentralized control. Existing average consensus algorithms require individual nodes (agents) to exchange explicit state values with their neighbors, which leads to the undesirable disclosure of sensitive information in the state. In our work, we propose a novel average consensus algorithm for time-varying directed graphs which can protect the privacy of participating nodes\u27 initial states. Leveraging algorithm-level obfuscation, the algorithm does not need the assistance of any trusted third party or data aggregator. By leveraging the inherent robustness of consensus dynamics against random variations in interaction, our proposed algorithm can guarantee privacy of participating nodes without compromising the accuracy of consensus. The algorithm is distinctly different from differential-privacy based average consensus approaches which enable privacy through compromising accuracy in obtained consensus value. The approach is able to protect the privacy of participating nodes even in the presence of multiple honest-but-curious nodes which can collude with each other
Data-true Characterization Of Neuronal Models
In this thesis, a weighted least squares approach is initially presented to estimate the parameters of an adaptive quadratic neuronal model. By casting the discontinuities in the state variables at the spiking instants as an impulse train driving the system dynamics, the neuronal output is represented as a linearly parameterized model that depends on filtered versions of the input current and the output voltage at the cell membrane. A prediction errorbased weighted least squares method is formulated for the model. This method allows for rapid estimation of model parameters under a persistently exciting input current injection. Simulation results show the feasibility of this approach to predict multiple neuronal firing patterns. Results of the method using data from a detailed ion-channel based model showed issues that served as the basis for the more robust resonate-and-fire model presented. A second method is proposed to overcome some of the issues found in the adaptive quadratic model presented. The original quadratic model is replaced by a linear resonateand-fire model -with stochastic threshold- that is both computational efficient and suitable for larger network simulations. The parameter estimation method presented here consists of different stages where the set of parameters is divided in to two. The first set of parameters is assumed to represent the subthreshold dynamics of the model, and it is estimated using a nonlinear least squares algorithm, while the second set is associated with the threshold and iii reset parameters as its estimated using maximum likelihood formulations. The validity of the estimation method is then tested using detailed Hodgkin-Huxley model data as well as experimental voltage recordings from rat motoneurons
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